Although Monte Carlo (MC) dropout [1]} probability is treated as a static hyperparameter in previous studies [2]}, [3]}, it plays an important role in the prediction outcome and can be leveraged to reduce the uncertainty of the prediction during the testing phase [4]}. Therefore, we rely on an automatic selection mechanism for the optimal dropout in online settings. Such selection is based on the uncertainty of the prediction produced during the testing phase (Figure REF ).
